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| """ | |
| Taken from ESPNet | |
| """ | |
| import torch | |
| from torch.functional import stft as torch_stft | |
| from torch_complex.tensor import ComplexTensor | |
| from Utility.utils import make_pad_mask | |
| class STFT(torch.nn.Module): | |
| def __init__(self, n_fft=512, win_length=None, hop_length=128, window="hann", center=True, normalized=False, | |
| onesided=True): | |
| super().__init__() | |
| self.n_fft = n_fft | |
| if win_length is None: | |
| self.win_length = n_fft | |
| else: | |
| self.win_length = win_length | |
| self.hop_length = hop_length | |
| self.center = center | |
| self.normalized = normalized | |
| self.onesided = onesided | |
| self.window = window | |
| def extra_repr(self): | |
| return (f"n_fft={self.n_fft}, " | |
| f"win_length={self.win_length}, " | |
| f"hop_length={self.hop_length}, " | |
| f"center={self.center}, " | |
| f"normalized={self.normalized}, " | |
| f"onesided={self.onesided}") | |
| def forward(self, input_wave, ilens=None): | |
| """ | |
| STFT forward function. | |
| Args: | |
| input_wave: (Batch, Nsamples) or (Batch, Nsample, Channels) | |
| ilens: (Batch) | |
| Returns: | |
| output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2) | |
| """ | |
| bs = input_wave.size(0) | |
| if input_wave.dim() == 3: | |
| multi_channel = True | |
| # input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample) | |
| input_wave = input_wave.transpose(1, 2).reshape(-1, input_wave.size(1)) | |
| else: | |
| multi_channel = False | |
| # output: (Batch, Freq, Frames, 2=real_imag) | |
| # or (Batch, Channel, Freq, Frames, 2=real_imag) | |
| if self.window is not None: | |
| window_func = getattr(torch, f"{self.window}_window") | |
| window = window_func(self.win_length, dtype=input_wave.dtype, device=input_wave.device) | |
| else: | |
| window = None | |
| complex_output = torch_stft(input=input_wave, | |
| n_fft=self.n_fft, | |
| win_length=self.win_length, | |
| hop_length=self.hop_length, | |
| center=self.center, | |
| window=window, | |
| normalized=self.normalized, | |
| onesided=self.onesided, | |
| return_complex=True) | |
| output = torch.view_as_real(complex_output) | |
| # output: (Batch, Freq, Frames, 2=real_imag) | |
| # -> (Batch, Frames, Freq, 2=real_imag) | |
| output = output.transpose(1, 2) | |
| if multi_channel: | |
| # output: (Batch * Channel, Frames, Freq, 2=real_imag) | |
| # -> (Batch, Frame, Channel, Freq, 2=real_imag) | |
| output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose(1, 2) | |
| if ilens is not None: | |
| if self.center: | |
| pad = self.win_length // 2 | |
| ilens = ilens + 2 * pad | |
| olens = (ilens - self.win_length) // self.hop_length + 1 | |
| output.masked_fill_(make_pad_mask(olens, output, 1), 0.0) | |
| else: | |
| olens = None | |
| return output, olens | |
| def inverse(self, input, ilens=None): | |
| """ | |
| Inverse STFT. | |
| Args: | |
| input: Tensor(batch, T, F, 2) or ComplexTensor(batch, T, F) | |
| ilens: (batch,) | |
| Returns: | |
| wavs: (batch, samples) | |
| ilens: (batch,) | |
| """ | |
| istft = torch.functional.istft | |
| if self.window is not None: | |
| window_func = getattr(torch, f"{self.window}_window") | |
| window = window_func(self.win_length, dtype=input.dtype, device=input.device) | |
| else: | |
| window = None | |
| if isinstance(input, ComplexTensor): | |
| input = torch.stack([input.real, input.imag], dim=-1) | |
| assert input.shape[-1] == 2 | |
| input = input.transpose(1, 2) | |
| wavs = istft(input, n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window=window, center=self.center, | |
| normalized=self.normalized, onesided=self.onesided, length=ilens.max() if ilens is not None else ilens) | |
| return wavs, ilens | |